{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 8 - MACHINE LEARNING WITH SCIKIT-LEARN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Iris Flower Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TPxlVnJgswel2MmBk74Q4JmCJ8+CTD04a9tWb6aXX4ekjFiRLawZWmsrulRzr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078cd87ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,0] #X-Axis - sepal lenght\n",
    "y = iris.data[:,1] #Y-Axis - sepal width\n",
    "species = iris.target #species\n",
    "\n",
    "x_min, x_max = x.min() - .5, x.max() + .5\n",
    "y_min, y_max = y.min() - .5, y.max() + .5\n",
    "\n",
    "#SCATTERPLOT\n",
    "plt.figure()\n",
    "plt.title('Iris Dataset - Classification By Sepal Size')\n",
    "plt.scatter(x,y, c=species)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4++Yf1ffkqOMP+jjn5h/VP5+w/46UDOn8+mN/0MdZNzZM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078baa5f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,2] #X-Axis - petal lenght\n",
    "y = iris.data[:,3] #Y-Axis - petal width\n",
    "species = iris.target #species\n",
    "\n",
    "x_min, x_max = x.min() - .5, x.max() + .5\n",
    "y_min, y_max = y.min() - .5, y.max() + .5\n",
    "\n",
    "#SCATTERPLOT\n",
    "plt.figure()\n",
    "plt.title('Iris Dataset - Classification By Petal Sizes', size = 14)\n",
    "plt.scatter(x,y, c=species)\n",
    "plt.xlabel('Petal length')\n",
    "plt.ylabel('Petal width')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The PCA Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ng2GYnM49npv08OHD81pK5Ru+x6PFYkFFRUXK4uWFzivMxAUr7nLBW0Jnogu2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078d68e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "#x = iris.data[:,1] #X-Axis - sepal width\n",
    "#y = iris.data[:,2] #Y-Axis - petal lenght\n",
    "species = iris.target #species\n",
    "x_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
    "\n",
    "#SCATTERPLOT 3D\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.set_title('Iris Dataset by PCA', size = 14)\n",
    "ax.scatter(x_reduced[:,0],x_reduced[:,1],x_reduced[:,2], c=species)\n",
    "ax.set_xlabel('First eigenvector')\n",
    "ax.set_ylabel('Second eigenvector')\n",
    "ax.set_zlabel('Third eigenvector')\n",
    "ax.w_xaxis.set_ticklabels(())\n",
    "ax.w_yaxis.set_ticklabels(())\n",
    "ax.w_zaxis.set_ticklabels(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "np.random.seed(0)\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data\n",
    "y = iris.target\n",
    "i = np.random.permutation(len(iris.data))\n",
    "x_train = x[i[:-10]]\n",
    "y_train = y[i[:-10]]\n",
    "x_test = x[i[-10:]]\n",
    "y_test = y[i[-10:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0, 2, 1, 2, 0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H9RHScdj4xBMJ+W07FmP9o39M23nX/vGRhMoVKxZlw+OP0VcvcMXgQY56HbQOg+h6HIat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\nESlDCncRkTKkcBcRKUMKdxGRMvR/KR3UoIOgoK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f441630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import datasets\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,:2] #X-Axis - sepal lenght-width\n",
    "y = iris.target     #Y-Axis - species\n",
    "\n",
    "x_min, x_max = x[:,0].min() - .5, x[:,0].max() + .5\n",
    "y_min, y_max = x[:,1].min() - .5, x[:,1].max() + .5\n",
    "\n",
    "#MESH\n",
    "cmap_light = ListedColormap(['#AAAAFF','#AAFFAA','#FFAAAA'])\n",
    "h = .02\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x,y)\n",
    "Z = knn.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx,yy,Z,cmap=cmap_light)\n",
    "\n",
    "#Plot the training points\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "plt.xlim(xx.min(),xx.max())\n",
    "plt.ylim(yy.min(),yy.max())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GojGPbz7UROPpKNGoEgwKAUf48B11NFRnVv8djyuxuBIOpd9X+rdHmnjtUOrZ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f4412b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import datasets\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,2:4] #X-Axis - petal lenght-width\n",
    "y = iris.target     #Y-Axis - species\n",
    "\n",
    "x_min, x_max = x[:,0].min() - .5, x[:,0].max() + .5\n",
    "y_min, y_max = x[:,1].min() - .5, x[:,1].max() + .5\n",
    "\n",
    "#MESH\n",
    "cmap_light = ListedColormap(['#AAAAFF','#AAFFAA','#FFAAAA'])\n",
    "h = .02\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x,y)\n",
    "Z = knn.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx,yy,Z,cmap=cmap_light)\n",
    "\n",
    "#Plot the training points\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "plt.xlim(xx.min(),xx.max())\n",
    "plt.ylim(yy.min(),yy.max())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diabetes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03807591,  0.05068012,  0.06169621,  0.02187235, -0.0442235 ,\n",
       "       -0.03482076, -0.04340085, -0.00259226,  0.01990842, -0.01764613])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000746"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(diabetes.data[:,0]**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 151.,   75.,  141.,  206.,  135.,   97.,  138.,   63.,  110.,\n",
       "        310.,  101.,   69.,  179.,  185.,  118.,  171.,  166.,  144.,\n",
       "         97.,  168.,   68.,   49.,   68.,  245.,  184.,  202.,  137.,\n",
       "         85.,  131.,  283.,  129.,   59.,  341.,   87.,   65.,  102.,\n",
       "        265.,  276.,  252.,   90.,  100.,   55.,   61.,   92.,  259.,\n",
       "         53.,  190.,  142.,   75.,  142.,  155.,  225.,   59.,  104.,\n",
       "        182.,  128.,   52.,   37.,  170.,  170.,   61.,  144.,   52.,\n",
       "        128.,   71.,  163.,  150.,   97.,  160.,  178.,   48.,  270.,\n",
       "        202.,  111.,   85.,   42.,  170.,  200.,  252.,  113.,  143.,\n",
       "         51.,   52.,  210.,   65.,  141.,   55.,  134.,   42.,  111.,\n",
       "         98.,  164.,   48.,   96.,   90.,  162.,  150.,  279.,   92.,\n",
       "         83.,  128.,  102.,  302.,  198.,   95.,   53.,  134.,  144.,\n",
       "        232.,   81.,  104.,   59.,  246.,  297.,  258.,  229.,  275.,\n",
       "        281.,  179.,  200.,  200.,  173.,  180.,   84.,  121.,  161.,\n",
       "         99.,  109.,  115.,  268.,  274.,  158.,  107.,   83.,  103.,\n",
       "        272.,   85.,  280.,  336.,  281.,  118.,  317.,  235.,   60.,\n",
       "        174.,  259.,  178.,  128.,   96.,  126.,  288.,   88.,  292.,\n",
       "         71.,  197.,  186.,   25.,   84.,   96.,  195.,   53.,  217.,\n",
       "        172.,  131.,  214.,   59.,   70.,  220.,  268.,  152.,   47.,\n",
       "         74.,  295.,  101.,  151.,  127.,  237.,  225.,   81.,  151.,\n",
       "        107.,   64.,  138.,  185.,  265.,  101.,  137.,  143.,  141.,\n",
       "         79.,  292.,  178.,   91.,  116.,   86.,  122.,   72.,  129.,\n",
       "        142.,   90.,  158.,   39.,  196.,  222.,  277.,   99.,  196.,\n",
       "        202.,  155.,   77.,  191.,   70.,   73.,   49.,   65.,  263.,\n",
       "        248.,  296.,  214.,  185.,   78.,   93.,  252.,  150.,   77.,\n",
       "        208.,   77.,  108.,  160.,   53.,  220.,  154.,  259.,   90.,\n",
       "        246.,  124.,   67.,   72.,  257.,  262.,  275.,  177.,   71.,\n",
       "         47.,  187.,  125.,   78.,   51.,  258.,  215.,  303.,  243.,\n",
       "         91.,  150.,  310.,  153.,  346.,   63.,   89.,   50.,   39.,\n",
       "        103.,  308.,  116.,  145.,   74.,   45.,  115.,  264.,   87.,\n",
       "        202.,  127.,  182.,  241.,   66.,   94.,  283.,   64.,  102.,\n",
       "        200.,  265.,   94.,  230.,  181.,  156.,  233.,   60.,  219.,\n",
       "         80.,   68.,  332.,  248.,   84.,  200.,   55.,   85.,   89.,\n",
       "         31.,  129.,   83.,  275.,   65.,  198.,  236.,  253.,  124.,\n",
       "         44.,  172.,  114.,  142.,  109.,  180.,  144.,  163.,  147.,\n",
       "         97.,  220.,  190.,  109.,  191.,  122.,  230.,  242.,  248.,\n",
       "        249.,  192.,  131.,  237.,   78.,  135.,  244.,  199.,  270.,\n",
       "        164.,   72.,   96.,  306.,   91.,  214.,   95.,  216.,  263.,\n",
       "        178.,  113.,  200.,  139.,  139.,   88.,  148.,   88.,  243.,\n",
       "         71.,   77.,  109.,  272.,   60.,   54.,  221.,   90.,  311.,\n",
       "        281.,  182.,  321.,   58.,  262.,  206.,  233.,  242.,  123.,\n",
       "        167.,   63.,  197.,   71.,  168.,  140.,  217.,  121.,  235.,\n",
       "        245.,   40.,   52.,  104.,  132.,   88.,   69.,  219.,   72.,\n",
       "        201.,  110.,   51.,  277.,   63.,  118.,   69.,  273.,  258.,\n",
       "         43.,  198.,  242.,  232.,  175.,   93.,  168.,  275.,  293.,\n",
       "        281.,   72.,  140.,  189.,  181.,  209.,  136.,  261.,  113.,\n",
       "        131.,  174.,  257.,   55.,   84.,   42.,  146.,  212.,  233.,\n",
       "         91.,  111.,  152.,  120.,   67.,  310.,   94.,  183.,   66.,\n",
       "        173.,   72.,   49.,   64.,   48.,  178.,  104.,  132.,  220.,   57.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression: The Least Square Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "linreg = linear_model.LinearRegression()\n",
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes()\n",
    "x_train = diabetes.data[:-20]\n",
    "y_train = diabetes.target[:-20]\n",
    "x_test = diabetes.data[-20:]\n",
    "y_test = diabetes.target[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.03499549e-01,  -2.37639315e+02,   5.10530605e+02,\n",
       "         3.27736980e+02,  -8.14131709e+02,   4.92814588e+02,\n",
       "         1.02848452e+02,   1.84606489e+02,   7.43519617e+02,\n",
       "         7.60951722e+01])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 197.61846908,  155.43979328,  172.88665147,  111.53537279,\n",
       "        164.80054784,  131.06954875,  259.12237761,  100.47935157,\n",
       "        117.0601052 ,  124.30503555,  218.36632793,   61.19831284,\n",
       "        132.25046751,  120.3332925 ,   52.54458691,  194.03798088,\n",
       "        102.57139702,  123.56604987,  211.0346317 ,   52.60335674])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 233.,   91.,  111.,  152.,  120.,   67.,  310.,   94.,  183.,\n",
       "         66.,  173.,   72.,   49.,   64.,   48.,  178.,  104.,  132.,\n",
       "        220.,   57.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58507530226905713"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f82eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes()\n",
    "x_train = diabetes.data[:-20]\n",
    "y_train = diabetes.target[:-20]\n",
    "x_test = diabetes.data[-20:]\n",
    "y_test = diabetes.target[-20:]\n",
    "\n",
    "x0_test = x_test[:,0]\n",
    "x0_train = x_train[:,0]\n",
    "x0_test = x0_test[:,np.newaxis]\n",
    "x0_train = x0_train[:,np.newaxis]\n",
    "linreg = linear_model.LinearRegression()\n",
    "linreg.fit(x0_train, y_train)\n",
    "y = linreg.predict(x0_test)\n",
    "plt.scatter(x0_test, y_test, color='k')\n",
    "plt.plot(x0_test, y, color='b', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fDRwGnGVmhwDnAJvcfS2wKb4PcCywNp6Ggc+1G5RIKy66aLZ7q1lS/vu/R8X9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\ntVWB16fW1lbt7u4eslypVBqyTHkqlUrJBB6RK66o3H+oPZ1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f825198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes()\n",
    "x_train = diabetes.data[:-20]\n",
    "y_train = diabetes.target[:-20]\n",
    "x_test = diabetes.data[-20:]\n",
    "y_test = diabetes.target[-20:]\n",
    "\n",
    "plt.figure(figsize=(8,12))\n",
    "for f in range(0,10):\n",
    "    xi_test = x_test[:,f]\n",
    "    xi_train = x_train[:,f]\n",
    "    xi_test = xi_test[:,np.newaxis]\n",
    "    xi_train = xi_train[:,np.newaxis]\n",
    "    linreg.fit(xi_train, y_train)\n",
    "    y = linreg.predict(xi_test)\n",
    "    plt.subplot(5,2,f+1)\n",
    "    plt.scatter(xi_test,y_test,color='k')\n",
    "    plt.plot(xi_test, y, color='b', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines (SVMs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Classification (SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y49xhkDuymdW1nFqO+GcDZwBvk/RAcjtJ0kclfTTpcwvwBLAK+C7wDwARsQH4PHB/crs4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\nzcxSxsFvZpYyDn4zs5T5/3e7jUbaXdE7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f82e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xsymrVq9MwZeWfPbA+HG8NfFpuvTctU6laCFfA8SqWRojFNva/o5UFvZqHttm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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f60b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "svc = svm.SVC(kernel='linear').fit(x,y)\n",
    "X,Y = np.mgrid[0:4:200j,0:4:200j]\n",
    "Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z > 0, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors=['k'], linestyles=['-'], levels=[0])\n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict([[1.5, 2.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict([[2.5, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KIvbiJThc6yaGj3eXog3o/ZYpS9GurVemSJeiqZg4AlduRcUlIyVDyWhzI19v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\nJqEHvqZpmknoga9pmmYSeuBrmqaZxP8BndDR/T87Kq4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f5fdf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "svc = svm.SVC(kernel='linear', C=1).fit(x,y)   # Ho tolto C=1\n",
    "X,Y = np.mgrid[0:4:200j,0:4:200j]\n",
    "Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z > 0, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'], levels=[-1,0,1])\n",
    "plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120, facecolors='r')  \n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8VFVLTsdY2ttav/cN1Smq2kVVuyTXCO8kQdHK7wuw7cstJNVPIOD/fu+WgC9A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\n3XEMi+3esoLC/ABPPT7B7iiGxUKhEFM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f8459e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "svc = svm.SVC(kernel='linear', C=0.1).fit(x,y)   # Ho tolto C=1\n",
    "X,Y = np.mgrid[0:4:200j,0:4:200j]\n",
    "Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z > 0, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'], levels=[-1,0,1])\n",
    "plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120, facecolors='w')  \n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonlinear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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unO+n9KR0mYIOy5EVpKngCyF0pBT7xVLK1Y8+//AbgJRyoxDiJyGEn5QyynZR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\nePW17LfTWGajCr6SZmazmZ1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fASEoUzPzLzR2I+wOAIH5n784ay0R94s9ILQgdCAEQibi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\nCG/4zjlXIf4DgcLaca3M3YUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078cb29dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "svc = svm.SVC(kernel='poly', C=1, degree=3).fit(x,y)   # Ho tolto C=1\n",
    "X,Y = np.mgrid[0:4:200j,0:4:200j]\n",
    "Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z > 0, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'], levels=[-1,0,1])\n",
    "plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120, facecolors='w')  \n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkX2QiAsRWzzG+PH6t1SZA7ZQtj78usCYkn58G/CZMWaKiAwDFhpjJgOjgY9EJI3iPfv+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\nK9xByx5J3Pu/izjjrPZVYo9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078faffb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],\n",
    "             [2,1],[3,1],[3,2],[3.5,1],[3.5,3]])\n",
    "y = [0]*6 + [1]*5\n",
    "svc = svm.SVC(kernel='rbf', C=1, gamma=3).fit(x,y)   # Ho tolto C=1\n",
    "X,Y = np.mgrid[0:4:200j,0:4:200j]\n",
    "Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z > 0, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'], levels=[-1,0,1])\n",
    "plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120, facecolors='w')  \n",
    "plt.scatter(x[:,0],x[:,1],c=y, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Different SVM Classifiers Using the Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QFKi0bVFuUYXHinMqPgZQmfBjwf7CmDYJTVCQnY8b45hruxRV0eeRN4zDOVrn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\nQzq5a5qmNUP/D08IMeM6WNKeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f8b1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,:2]\n",
    "y = iris.target\n",
    "h = .05\n",
    "svc = svm.SVC(kernel='linear',C=1.0).fit(x,y)\n",
    "x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5\n",
    "y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5\n",
    "h = .02\n",
    "X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = svc.predict(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors='k')\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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edjLdBgZT8AzDYOF3i9j410ZcMS5OGd6HNj1aha7ftzWNNX+swQiYtD+5LS26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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f602588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,:2]\n",
    "y = iris.target\n",
    "h = .05\n",
    "svc = svm.SVC(kernel='poly',C=1.0, degree=3).fit(x,y)\n",
    "x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5\n",
    "y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5\n",
    "h = .02\n",
    "X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = svc.predict(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors='k')\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ebds89NSf+WLORwCcfdnl3Pef/6LE8KfvPWQoaa1CBmlz56zg3rvfPu4YHByq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078d68ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:,:2]\n",
    "y = iris.target\n",
    "h = .05\n",
    "svc = svm.SVC(kernel='rbf',C=1.0, gamma=3).fit(x,y)\n",
    "x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5\n",
    "y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5\n",
    "h = .02\n",
    "X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = svc.predict(np.c_[X.ravel(),Y.ravel()])\n",
    "Z = Z.reshape(X.shape)\n",
    "plt.contourf(X,Y,Z, alpha=0.4)\n",
    "plt.contour(X,Y,Z,colors='k')\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Regression (SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "x_train = diabetes.data[:-20]\n",
    "y_train = diabetes.target[:-20]\n",
    "x_test = diabetes.data[-20:]\n",
    "y_test = diabetes.target[-20:]\n",
    "\n",
    "x0_test = x_test[:,2]\n",
    "x0_train = x_train[:,2]\n",
    "x0_test = x0_test[:,np.newaxis]\n",
    "x0_train = x0_train[:,np.newaxis]\n",
    "x0_test.sort(axis=0)\n",
    "x0_test = x0_test*100\n",
    "x0_train = x0_train*100\n",
    "\n",
    "svr = svm.SVR(kernel='linear', C=1000)\n",
    "svr2 = svm.SVR(kernel='poly', C=1000, degree=2)\n",
    "svr3 = svm.SVR(kernel='poly', C=1000, degree=3)\n",
    "svr.fit(x0_train,y_train)\n",
    "svr2.fit(x0_train,y_train)\n",
    "svr3.fit(x0_train,y_train)\n",
    "y = svr.predict(x0_test)\n",
    "y2 = svr.predict(x0_test)\n",
    "y3 = svr.predict(x0_test)\n",
    "plt.plot(x0_test, y, color='k')\n",
    "plt.plot(x0_test, y2, c='r')\n",
    "plt.plot(x0_test, y3, c='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
